{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 获取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "def load_housing_data(housing_path = './'):\n",
    "    csv_path = os.path.join(housing_path,'housing.csv')\n",
    "    return pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = load_housing_data()\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           20640 non-null  float64\n",
      " 1   latitude            20640 non-null  float64\n",
      " 2   housing_median_age  20640 non-null  float64\n",
      " 3   total_rooms         20640 non-null  float64\n",
      " 4   total_bedrooms      20433 non-null  float64\n",
      " 5   population          20640 non-null  float64\n",
      " 6   households          20640 non-null  float64\n",
      " 7   median_income       20640 non-null  float64\n",
      " 8   median_house_value  20640 non-null  float64\n",
      " 9   ocean_proximity     20640 non-null  object \n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info() #得到数据类型和非空值数量 total_bedrooms有空值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.groupby(['ocean_proximity']).groups.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['ocean_proximity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>&lt;1H OCEAN</th>\n",
       "      <td>9136</td>\n",
       "      <td>9136</td>\n",
       "      <td>9136</td>\n",
       "      <td>9136</td>\n",
       "      <td>9034</td>\n",
       "      <td>9136</td>\n",
       "      <td>9136</td>\n",
       "      <td>9136</td>\n",
       "      <td>9136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INLAND</th>\n",
       "      <td>6551</td>\n",
       "      <td>6551</td>\n",
       "      <td>6551</td>\n",
       "      <td>6551</td>\n",
       "      <td>6496</td>\n",
       "      <td>6551</td>\n",
       "      <td>6551</td>\n",
       "      <td>6551</td>\n",
       "      <td>6551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ISLAND</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEAR BAY</th>\n",
       "      <td>2290</td>\n",
       "      <td>2290</td>\n",
       "      <td>2290</td>\n",
       "      <td>2290</td>\n",
       "      <td>2270</td>\n",
       "      <td>2290</td>\n",
       "      <td>2290</td>\n",
       "      <td>2290</td>\n",
       "      <td>2290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEAR OCEAN</th>\n",
       "      <td>2658</td>\n",
       "      <td>2658</td>\n",
       "      <td>2658</td>\n",
       "      <td>2658</td>\n",
       "      <td>2628</td>\n",
       "      <td>2658</td>\n",
       "      <td>2658</td>\n",
       "      <td>2658</td>\n",
       "      <td>2658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 longitude  latitude  housing_median_age  total_rooms  \\\n",
       "ocean_proximity                                                         \n",
       "<1H OCEAN             9136      9136                9136         9136   \n",
       "INLAND                6551      6551                6551         6551   \n",
       "ISLAND                   5         5                   5            5   \n",
       "NEAR BAY              2290      2290                2290         2290   \n",
       "NEAR OCEAN            2658      2658                2658         2658   \n",
       "\n",
       "                 total_bedrooms  population  households  median_income  \\\n",
       "ocean_proximity                                                          \n",
       "<1H OCEAN                  9034        9136        9136           9136   \n",
       "INLAND                     6496        6551        6551           6551   \n",
       "ISLAND                        5           5           5              5   \n",
       "NEAR BAY                   2270        2290        2290           2290   \n",
       "NEAR OCEAN                 2628        2658        2658           2658   \n",
       "\n",
       "                 median_house_value  \n",
       "ocean_proximity                      \n",
       "<1H OCEAN                      9136  \n",
       "INLAND                         6551  \n",
       "ISLAND                            5  \n",
       "NEAR BAY                       2290  \n",
       "NEAR OCEAN                     2658  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.groupby(['ocean_proximity']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "housing.hist(bins = 50, figsize =(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 房龄和房价被设定了上限值：\n",
    "    1. 对于上限重新收集标签值；\n",
    "    2.把50w以上的区域的数据从训练集合和测试集合中移除\n",
    "\n",
    "### 重尾现象\n",
    "    *转换数据，把数据形状变成偏钟型分布\n",
    "\n",
    "### 收入特征\n",
    "    年薪，单位：万美元；需要知道数据是如何缩放的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 创建训练集合和测试集合\n",
    "    *训练集 80% 测试集 20%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def split_train_test(data,test_ratio):\n",
    "    np.random.seed(1)\n",
    "    indices = np.random.permutation(len(data)) #对数组打乱顺序 返回乱序的index\n",
    "    test_set_size = int(len(data)*test_ratio)\n",
    "    test_indeces = indices[:test_set_size]\n",
    "    train_indeces = indices[test_set_size:]\n",
    "    return data.iloc[train_indeces],data.iloc[test_indeces]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15961</th>\n",
       "      <td>-122.43</td>\n",
       "      <td>37.71</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1410.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>879.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>3.1908</td>\n",
       "      <td>255600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1771</th>\n",
       "      <td>-122.35</td>\n",
       "      <td>37.95</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1485.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>971.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>3.6094</td>\n",
       "      <td>114600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16414</th>\n",
       "      <td>-121.24</td>\n",
       "      <td>37.90</td>\n",
       "      <td>16.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.6250</td>\n",
       "      <td>137500.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5056</th>\n",
       "      <td>-118.35</td>\n",
       "      <td>34.02</td>\n",
       "      <td>34.0</td>\n",
       "      <td>5218.0</td>\n",
       "      <td>1576.0</td>\n",
       "      <td>3538.0</td>\n",
       "      <td>1371.0</td>\n",
       "      <td>1.5143</td>\n",
       "      <td>118800.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8589</th>\n",
       "      <td>-118.39</td>\n",
       "      <td>33.89</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1851.0</td>\n",
       "      <td>332.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>314.0</td>\n",
       "      <td>7.3356</td>\n",
       "      <td>422700.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "15961    -122.43     37.71                52.0       1410.0           286.0   \n",
       "1771     -122.35     37.95                42.0       1485.0           290.0   \n",
       "16414    -121.24     37.90                16.0         50.0            10.0   \n",
       "5056     -118.35     34.02                34.0       5218.0          1576.0   \n",
       "8589     -118.39     33.89                38.0       1851.0           332.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "15961       879.0       282.0         3.1908            255600.0   \n",
       "1771        971.0       303.0         3.6094            114600.0   \n",
       "16414        20.0         6.0         2.6250            137500.0   \n",
       "5056       3538.0      1371.0         1.5143            118800.0   \n",
       "8589        750.0       314.0         7.3356            422700.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "15961        NEAR BAY  \n",
       "1771         NEAR BAY  \n",
       "16414          INLAND  \n",
       "5056        <1H OCEAN  \n",
       "8589        <1H OCEAN  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set,test_set = split_train_test(housing,0.2)\n",
    "train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16512"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4128"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 为了保证有数据更新的情况下不影响训练集和数据集的数据分布，对样本设置标识符。\n",
    "# 取标识符的hash值，hash值的最后一个字节，如果值小于51（256*0.2），则放入测试集\n",
    "\n",
    "import hashlib\n",
    "\n",
    "def test_set_check(identifier,test_ratio,hash=hashlib.md5):\n",
    "    return hash(np.int64(identifier)).digest()[-1] < 256 * test_ratio #返回二进制字符串\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_train_test_by_id(data, test_ratio, id_column):\n",
    "    ids = data[id_column]\n",
    "    in_test_set = ids.apply(lambda id_:test_set_check(id_, test_ratio))\n",
    "    return data.loc[-in_test_set],data.loc[in_test_set]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_with_id = housing.reset_index() #添加行索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "4      4    -122.25     37.85                52.0       1627.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "4           280.0       565.0       259.0         3.8462            342200.0   \n",
       "\n",
       "  ocean_proximity  \n",
       "0        NEAR BAY  \n",
       "1        NEAR BAY  \n",
       "2        NEAR BAY  \n",
       "3        NEAR BAY  \n",
       "4        NEAR BAY  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_with_id.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, 'index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>919.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>4.0368</td>\n",
       "      <td>269700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3503.0</td>\n",
       "      <td>752.0</td>\n",
       "      <td>1504.0</td>\n",
       "      <td>734.0</td>\n",
       "      <td>3.2705</td>\n",
       "      <td>241800.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.85</td>\n",
       "      <td>40.0</td>\n",
       "      <td>751.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>1.3578</td>\n",
       "      <td>147500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1688.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>853.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>2.1806</td>\n",
       "      <td>99700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20619</th>\n",
       "      <td>20619</td>\n",
       "      <td>-121.56</td>\n",
       "      <td>39.01</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1891.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>2.7303</td>\n",
       "      <td>99100.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20625</th>\n",
       "      <td>20625</td>\n",
       "      <td>-121.52</td>\n",
       "      <td>39.12</td>\n",
       "      <td>37.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.1250</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20632</th>\n",
       "      <td>20632</td>\n",
       "      <td>-121.45</td>\n",
       "      <td>39.26</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2319.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>1047.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>3.1250</td>\n",
       "      <td>115600.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20633</th>\n",
       "      <td>20633</td>\n",
       "      <td>-121.53</td>\n",
       "      <td>39.19</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2080.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>1082.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>2.5495</td>\n",
       "      <td>98300.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20637</th>\n",
       "      <td>20637</td>\n",
       "      <td>-121.22</td>\n",
       "      <td>39.43</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2254.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>1.7000</td>\n",
       "      <td>92300.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4278 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "4          4    -122.25     37.85                52.0       1627.0   \n",
       "5          5    -122.25     37.85                52.0        919.0   \n",
       "11        11    -122.26     37.85                52.0       3503.0   \n",
       "20        20    -122.27     37.85                40.0        751.0   \n",
       "23        23    -122.27     37.84                52.0       1688.0   \n",
       "...      ...        ...       ...                 ...          ...   \n",
       "20619  20619    -121.56     39.01                22.0       1891.0   \n",
       "20625  20625    -121.52     39.12                37.0        102.0   \n",
       "20632  20632    -121.45     39.26                15.0       2319.0   \n",
       "20633  20633    -121.53     39.19                27.0       2080.0   \n",
       "20637  20637    -121.22     39.43                17.0       2254.0   \n",
       "\n",
       "       total_bedrooms  population  households  median_income  \\\n",
       "4               280.0       565.0       259.0         3.8462   \n",
       "5               213.0       413.0       193.0         4.0368   \n",
       "11              752.0      1504.0       734.0         3.2705   \n",
       "20              184.0       409.0       166.0         1.3578   \n",
       "23              337.0       853.0       325.0         2.1806   \n",
       "...               ...         ...         ...            ...   \n",
       "20619           340.0      1023.0       296.0         2.7303   \n",
       "20625            17.0        29.0        14.0         4.1250   \n",
       "20632           416.0      1047.0       385.0         3.1250   \n",
       "20633           412.0      1082.0       382.0         2.5495   \n",
       "20637           485.0      1007.0       433.0         1.7000   \n",
       "\n",
       "       median_house_value ocean_proximity  \n",
       "4                342200.0        NEAR BAY  \n",
       "5                269700.0        NEAR BAY  \n",
       "11               241800.0        NEAR BAY  \n",
       "20               147500.0        NEAR BAY  \n",
       "23                99700.0        NEAR BAY  \n",
       "...                   ...             ...  \n",
       "20619             99100.0          INLAND  \n",
       "20625             72000.0          INLAND  \n",
       "20632            115600.0          INLAND  \n",
       "20633             98300.0          INLAND  \n",
       "20637             92300.0          INLAND  \n",
       "\n",
       "[4278 rows x 11 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#基于行索引会受限于数据增删改变，找更稳定的特征\n",
    "#使用经纬度做特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122192.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122182.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122202.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "4      4    -122.25     37.85                52.0       1627.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "4           280.0       565.0       259.0         3.8462            342200.0   \n",
       "\n",
       "  ocean_proximity         id  \n",
       "0        NEAR BAY -122192.12  \n",
       "1        NEAR BAY -122182.14  \n",
       "2        NEAR BAY -122202.15  \n",
       "3        NEAR BAY -122212.15  \n",
       "4        NEAR BAY -122212.15  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_with_id['id'] = housing['longitude']*1000+housing['latitude']\n",
    "housing_with_id.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.84</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2555.0</td>\n",
       "      <td>665.0</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>595.0</td>\n",
       "      <td>2.0804</td>\n",
       "      <td>226700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2202.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>910.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>3.2031</td>\n",
       "      <td>281500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3503.0</td>\n",
       "      <td>752.0</td>\n",
       "      <td>1504.0</td>\n",
       "      <td>734.0</td>\n",
       "      <td>3.2705</td>\n",
       "      <td>241800.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2491.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>3.0750</td>\n",
       "      <td>213500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>2.6736</td>\n",
       "      <td>191300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "8       8    -122.26     37.84                42.0       2555.0   \n",
       "10     10    -122.26     37.85                52.0       2202.0   \n",
       "11     11    -122.26     37.85                52.0       3503.0   \n",
       "12     12    -122.26     37.85                52.0       2491.0   \n",
       "13     13    -122.26     37.84                52.0        696.0   \n",
       "\n",
       "    total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "8            665.0      1206.0       595.0         2.0804            226700.0   \n",
       "10           434.0       910.0       402.0         3.2031            281500.0   \n",
       "11           752.0      1504.0       734.0         3.2705            241800.0   \n",
       "12           474.0      1098.0       468.0         3.0750            213500.0   \n",
       "13           191.0       345.0       174.0         2.6736            191300.0   \n",
       "\n",
       "   ocean_proximity         id  \n",
       "8         NEAR BAY -122222.16  \n",
       "10        NEAR BAY -122222.15  \n",
       "11        NEAR BAY -122222.15  \n",
       "12        NEAR BAY -122222.15  \n",
       "13        NEAR BAY -122222.16  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, 'id')\n",
    "test_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15961</th>\n",
       "      <td>-122.43</td>\n",
       "      <td>37.71</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1410.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>879.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>3.1908</td>\n",
       "      <td>255600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1771</th>\n",
       "      <td>-122.35</td>\n",
       "      <td>37.95</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1485.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>971.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>3.6094</td>\n",
       "      <td>114600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16414</th>\n",
       "      <td>-121.24</td>\n",
       "      <td>37.90</td>\n",
       "      <td>16.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.6250</td>\n",
       "      <td>137500.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5056</th>\n",
       "      <td>-118.35</td>\n",
       "      <td>34.02</td>\n",
       "      <td>34.0</td>\n",
       "      <td>5218.0</td>\n",
       "      <td>1576.0</td>\n",
       "      <td>3538.0</td>\n",
       "      <td>1371.0</td>\n",
       "      <td>1.5143</td>\n",
       "      <td>118800.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8589</th>\n",
       "      <td>-118.39</td>\n",
       "      <td>33.89</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1851.0</td>\n",
       "      <td>332.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>314.0</td>\n",
       "      <td>7.3356</td>\n",
       "      <td>422700.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "15961    -122.43     37.71                52.0       1410.0           286.0   \n",
       "1771     -122.35     37.95                42.0       1485.0           290.0   \n",
       "16414    -121.24     37.90                16.0         50.0            10.0   \n",
       "5056     -118.35     34.02                34.0       5218.0          1576.0   \n",
       "8589     -118.39     33.89                38.0       1851.0           332.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "15961       879.0       282.0         3.1908            255600.0   \n",
       "1771        971.0       303.0         3.6094            114600.0   \n",
       "16414        20.0         6.0         2.6250            137500.0   \n",
       "5056       3538.0      1371.0         1.5143            118800.0   \n",
       "8589        750.0       314.0         7.3356            422700.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "15961        NEAR BAY  \n",
       "1771         NEAR BAY  \n",
       "16414          INLAND  \n",
       "5056        <1H OCEAN  \n",
       "8589        <1H OCEAN  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_set, test_set = train_test_split(housing, test_size = 0.2, random_state = 1)\n",
    "train_set.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 分层采样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f0394f0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing['median_income'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    1. 创建一个收入类别属性\n",
    "    2. 中位数收入除以1.5，现实类别数量，大于5划归为5\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5.0\n",
       "1     5.0\n",
       "2     5.0\n",
       "3     4.0\n",
       "4     3.0\n",
       "5     3.0\n",
       "6     3.0\n",
       "7     3.0\n",
       "8     2.0\n",
       "9     3.0\n",
       "10    3.0\n",
       "11    3.0\n",
       "12    3.0\n",
       "13    2.0\n",
       "14    2.0\n",
       "15    2.0\n",
       "16    2.0\n",
       "17    2.0\n",
       "18    2.0\n",
       "19    2.0\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'] = np.ceil(housing['median_income']/1.5)\n",
    "\n",
    "housing['income_cat'].where(housing['income_cat']<5, 5.0, inplace = True)\n",
    "housing['income_cat'].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5\n",
       "1    5\n",
       "2    5\n",
       "3    4\n",
       "4    3\n",
       "Name: income_cat, dtype: category\n",
       "Categories (5, int64): [1 < 2 < 3 < 4 < 5]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'] = pd.cut(housing['median_income'], bins=[0.,1.5,3.0,4.5,6.,np.inf],labels=[1,2,3,4,5]) #把连续值转为标签\n",
    "housing['income_cat'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11ef8caf0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing['income_cat'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#根据收入类别进行分层采样\n",
    "from sklearn.model_selection import StratifiedShuffleSplit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "split = StratifiedShuffleSplit(n_splits=1, test_size= 0.2, random_state= 1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "for train_index, test_index in split.split(housing, housing['income_cat']):\n",
    "    strat_train_set = housing.loc[train_index]\n",
    "    strat_test_set = housing.loc[test_index]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>-122.13</td>\n",
       "      <td>37.67</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1748.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>914.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>3.8676</td>\n",
       "      <td>184000.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19529</th>\n",
       "      <td>-120.98</td>\n",
       "      <td>37.65</td>\n",
       "      <td>40.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>7.3841</td>\n",
       "      <td>172200.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8613</th>\n",
       "      <td>-118.37</td>\n",
       "      <td>33.87</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1829.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>891.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>6.5755</td>\n",
       "      <td>359900.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10142</th>\n",
       "      <td>-117.89</td>\n",
       "      <td>33.90</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1533.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>693.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>7.8980</td>\n",
       "      <td>258200.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15867</th>\n",
       "      <td>-122.40</td>\n",
       "      <td>37.76</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1529.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>1347.0</td>\n",
       "      <td>348.0</td>\n",
       "      <td>2.9312</td>\n",
       "      <td>239100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7803</th>\n",
       "      <td>-118.10</td>\n",
       "      <td>33.90</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1880.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>1229.0</td>\n",
       "      <td>378.0</td>\n",
       "      <td>4.4167</td>\n",
       "      <td>174600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19996</th>\n",
       "      <td>-119.45</td>\n",
       "      <td>36.16</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2119.0</td>\n",
       "      <td>373.0</td>\n",
       "      <td>1268.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>2.8152</td>\n",
       "      <td>106900.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9928</th>\n",
       "      <td>-122.31</td>\n",
       "      <td>38.30</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3023.0</td>\n",
       "      <td>659.0</td>\n",
       "      <td>1789.0</td>\n",
       "      <td>657.0</td>\n",
       "      <td>3.6039</td>\n",
       "      <td>126000.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12666</th>\n",
       "      <td>-121.45</td>\n",
       "      <td>38.48</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2780.0</td>\n",
       "      <td>510.0</td>\n",
       "      <td>1638.0</td>\n",
       "      <td>533.0</td>\n",
       "      <td>2.9571</td>\n",
       "      <td>103100.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2873</th>\n",
       "      <td>-118.96</td>\n",
       "      <td>35.37</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>1066.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>1.7467</td>\n",
       "      <td>52400.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8297</th>\n",
       "      <td>-118.14</td>\n",
       "      <td>33.76</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2960.0</td>\n",
       "      <td>761.0</td>\n",
       "      <td>1179.0</td>\n",
       "      <td>718.0</td>\n",
       "      <td>3.5214</td>\n",
       "      <td>398100.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19509</th>\n",
       "      <td>-121.02</td>\n",
       "      <td>37.64</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1437.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>1035.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>2.1036</td>\n",
       "      <td>88300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7698</th>\n",
       "      <td>-118.12</td>\n",
       "      <td>33.96</td>\n",
       "      <td>38.0</td>\n",
       "      <td>2105.0</td>\n",
       "      <td>348.0</td>\n",
       "      <td>956.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>4.4125</td>\n",
       "      <td>246000.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5256</th>\n",
       "      <td>-118.48</td>\n",
       "      <td>34.07</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4042.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>1318.0</td>\n",
       "      <td>542.0</td>\n",
       "      <td>12.8665</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8203</th>\n",
       "      <td>-118.15</td>\n",
       "      <td>33.78</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1584.0</td>\n",
       "      <td>435.0</td>\n",
       "      <td>904.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>2.0875</td>\n",
       "      <td>181300.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1609</th>\n",
       "      <td>-122.07</td>\n",
       "      <td>37.86</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1025.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>3.1200</td>\n",
       "      <td>155000.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11085</th>\n",
       "      <td>-117.89</td>\n",
       "      <td>33.84</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3315.0</td>\n",
       "      <td>744.0</td>\n",
       "      <td>2425.0</td>\n",
       "      <td>687.0</td>\n",
       "      <td>3.5521</td>\n",
       "      <td>182800.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11749</th>\n",
       "      <td>-121.15</td>\n",
       "      <td>38.80</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2104.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>745.0</td>\n",
       "      <td>314.0</td>\n",
       "      <td>4.1685</td>\n",
       "      <td>217500.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9200</th>\n",
       "      <td>-119.68</td>\n",
       "      <td>37.35</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2307.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>925.0</td>\n",
       "      <td>347.0</td>\n",
       "      <td>3.1326</td>\n",
       "      <td>119800.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5185</th>\n",
       "      <td>-118.26</td>\n",
       "      <td>33.94</td>\n",
       "      <td>44.0</td>\n",
       "      <td>795.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>716.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>90300.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "740      -122.13     37.67                40.0       1748.0           318.0   \n",
       "19529    -120.98     37.65                40.0        422.0            63.0   \n",
       "8613     -118.37     33.87                23.0       1829.0           331.0   \n",
       "10142    -117.89     33.90                23.0       1533.0           226.0   \n",
       "15867    -122.40     37.76                52.0       1529.0           385.0   \n",
       "7803     -118.10     33.90                40.0       1880.0           377.0   \n",
       "19996    -119.45     36.16                27.0       2119.0           373.0   \n",
       "9928     -122.31     38.30                45.0       3023.0           659.0   \n",
       "12666    -121.45     38.48                28.0       2780.0           510.0   \n",
       "2873     -118.96     35.37                41.0       1463.0           339.0   \n",
       "8297     -118.14     33.76                50.0       2960.0           761.0   \n",
       "19509    -121.02     37.64                42.0       1437.0           307.0   \n",
       "7698     -118.12     33.96                38.0       2105.0           348.0   \n",
       "5256     -118.48     34.07                37.0       4042.0           549.0   \n",
       "8203     -118.15     33.78                17.0       1584.0           435.0   \n",
       "1609     -122.07     37.86                23.0       1025.0           205.0   \n",
       "11085    -117.89     33.84                35.0       3315.0           744.0   \n",
       "11749    -121.15     38.80                20.0       2104.0           370.0   \n",
       "9200     -119.68     37.35                13.0       2307.0           386.0   \n",
       "5185     -118.26     33.94                44.0        795.0           181.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "740         914.0       317.0         3.8676            184000.0   \n",
       "19529       158.0        63.0         7.3841            172200.0   \n",
       "8613        891.0       356.0         6.5755            359900.0   \n",
       "10142       693.0       230.0         7.8980            258200.0   \n",
       "15867      1347.0       348.0         2.9312            239100.0   \n",
       "7803       1229.0       378.0         4.4167            174600.0   \n",
       "19996      1268.0       345.0         2.8152            106900.0   \n",
       "9928       1789.0       657.0         3.6039            126000.0   \n",
       "12666      1638.0       533.0         2.9571            103100.0   \n",
       "2873       1066.0       318.0         1.7467             52400.0   \n",
       "8297       1179.0       718.0         3.5214            398100.0   \n",
       "19509      1035.0       284.0         2.1036             88300.0   \n",
       "7698        956.0       350.0         4.4125            246000.0   \n",
       "5256       1318.0       542.0        12.8665            500001.0   \n",
       "8203        904.0       406.0         2.0875            181300.0   \n",
       "1609        263.0       191.0         3.1200            155000.0   \n",
       "11085      2425.0       687.0         3.5521            182800.0   \n",
       "11749       745.0       314.0         4.1685            217500.0   \n",
       "9200        925.0       347.0         3.1326            119800.0   \n",
       "5185        716.0       167.0         2.0000             90300.0   \n",
       "\n",
       "      ocean_proximity income_cat  \n",
       "740          NEAR BAY          3  \n",
       "19529          INLAND          5  \n",
       "8613        <1H OCEAN          5  \n",
       "10142       <1H OCEAN          5  \n",
       "15867        NEAR BAY          2  \n",
       "7803        <1H OCEAN          3  \n",
       "19996          INLAND          2  \n",
       "9928         NEAR BAY          3  \n",
       "12666          INLAND          2  \n",
       "2873           INLAND          2  \n",
       "8297       NEAR OCEAN          3  \n",
       "19509          INLAND          2  \n",
       "7698        <1H OCEAN          3  \n",
       "5256        <1H OCEAN          5  \n",
       "8203       NEAR OCEAN          2  \n",
       "1609         NEAR BAY          3  \n",
       "11085       <1H OCEAN          3  \n",
       "11749          INLAND          3  \n",
       "9200           INLAND          3  \n",
       "5185        <1H OCEAN          2  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_train_set.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.350533\n",
       "2    0.318798\n",
       "4    0.176357\n",
       "5    0.114583\n",
       "1    0.039729\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#分层采样验证\n",
    "strat_test_set['income_cat'].value_counts()/len(strat_test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.350581\n",
       "2    0.318847\n",
       "4    0.176308\n",
       "5    0.114438\n",
       "1    0.039826\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].value_counts()/len(housing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 将地理数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11efae430>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind = 'scatter', x='longitude', y='latitude', alpha=0.4, s = housing['population']/100, \n",
    "             label ='population', figsize = (10,7),c='median_house_value', cmap = plt.get_cmap('jet'),\n",
    "            colorbar=True,sharex=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.image as mpimg\n",
    "\n",
    "california_img = mpimg.imread('./california.png')\n",
    "housing.plot(kind = 'scatter', x='longitude', y='latitude', alpha=0.4, s = housing['population']/100, \n",
    "             label ='population', figsize = (10,7),c='median_house_value', cmap = plt.get_cmap('jet'),\n",
    "            colorbar=False,sharex=False)\n",
    "\n",
    "plt.imshow(california_img,extent=[-125.2,-113.5,32.5,42.2],alpha=0.5,cmap=plt.get_cmap('jet'))\n",
    "\n",
    "prices = housing['median_house_value']\n",
    "tick_values = np.linspace(prices.min(),prices.max(),11)\n",
    "cbar = plt.colorbar()\n",
    "cbar.ax.set_yticklabels = ([\"$dk\"%(round(v/1000))for v in tick_values])\n",
    "cbar.set_label('Median House Value', fontsize = 14)\n",
    "\n",
    "plt.legend(fontsize = 14)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 寻找特征相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.924664</td>\n",
       "      <td>-0.108197</td>\n",
       "      <td>0.044568</td>\n",
       "      <td>0.069608</td>\n",
       "      <td>0.099773</td>\n",
       "      <td>0.055310</td>\n",
       "      <td>-0.015176</td>\n",
       "      <td>-0.045967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>-0.924664</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011173</td>\n",
       "      <td>-0.036100</td>\n",
       "      <td>-0.066983</td>\n",
       "      <td>-0.108785</td>\n",
       "      <td>-0.071035</td>\n",
       "      <td>-0.079809</td>\n",
       "      <td>-0.144160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing_median_age</th>\n",
       "      <td>-0.108197</td>\n",
       "      <td>0.011173</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.361262</td>\n",
       "      <td>-0.320451</td>\n",
       "      <td>-0.296244</td>\n",
       "      <td>-0.302916</td>\n",
       "      <td>-0.119034</td>\n",
       "      <td>0.105623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rooms</th>\n",
       "      <td>0.044568</td>\n",
       "      <td>-0.036100</td>\n",
       "      <td>-0.361262</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.930380</td>\n",
       "      <td>0.857126</td>\n",
       "      <td>0.918484</td>\n",
       "      <td>0.198050</td>\n",
       "      <td>0.134153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bedrooms</th>\n",
       "      <td>0.069608</td>\n",
       "      <td>-0.066983</td>\n",
       "      <td>-0.320451</td>\n",
       "      <td>0.930380</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.877747</td>\n",
       "      <td>0.979728</td>\n",
       "      <td>-0.007723</td>\n",
       "      <td>0.049686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>0.099773</td>\n",
       "      <td>-0.108785</td>\n",
       "      <td>-0.296244</td>\n",
       "      <td>0.857126</td>\n",
       "      <td>0.877747</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.907222</td>\n",
       "      <td>0.004834</td>\n",
       "      <td>-0.024650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>households</th>\n",
       "      <td>0.055310</td>\n",
       "      <td>-0.071035</td>\n",
       "      <td>-0.302916</td>\n",
       "      <td>0.918484</td>\n",
       "      <td>0.979728</td>\n",
       "      <td>0.907222</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013033</td>\n",
       "      <td>0.065843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_income</th>\n",
       "      <td>-0.015176</td>\n",
       "      <td>-0.079809</td>\n",
       "      <td>-0.119034</td>\n",
       "      <td>0.198050</td>\n",
       "      <td>-0.007723</td>\n",
       "      <td>0.004834</td>\n",
       "      <td>0.013033</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.688075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_house_value</th>\n",
       "      <td>-0.045967</td>\n",
       "      <td>-0.144160</td>\n",
       "      <td>0.105623</td>\n",
       "      <td>0.134153</td>\n",
       "      <td>0.049686</td>\n",
       "      <td>-0.024650</td>\n",
       "      <td>0.065843</td>\n",
       "      <td>0.688075</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    longitude  latitude  housing_median_age  total_rooms  \\\n",
       "longitude            1.000000 -0.924664           -0.108197     0.044568   \n",
       "latitude            -0.924664  1.000000            0.011173    -0.036100   \n",
       "housing_median_age  -0.108197  0.011173            1.000000    -0.361262   \n",
       "total_rooms          0.044568 -0.036100           -0.361262     1.000000   \n",
       "total_bedrooms       0.069608 -0.066983           -0.320451     0.930380   \n",
       "population           0.099773 -0.108785           -0.296244     0.857126   \n",
       "households           0.055310 -0.071035           -0.302916     0.918484   \n",
       "median_income       -0.015176 -0.079809           -0.119034     0.198050   \n",
       "median_house_value  -0.045967 -0.144160            0.105623     0.134153   \n",
       "\n",
       "                    total_bedrooms  population  households  median_income  \\\n",
       "longitude                 0.069608    0.099773    0.055310      -0.015176   \n",
       "latitude                 -0.066983   -0.108785   -0.071035      -0.079809   \n",
       "housing_median_age       -0.320451   -0.296244   -0.302916      -0.119034   \n",
       "total_rooms               0.930380    0.857126    0.918484       0.198050   \n",
       "total_bedrooms            1.000000    0.877747    0.979728      -0.007723   \n",
       "population                0.877747    1.000000    0.907222       0.004834   \n",
       "households                0.979728    0.907222    1.000000       0.013033   \n",
       "median_income            -0.007723    0.004834    0.013033       1.000000   \n",
       "median_house_value        0.049686   -0.024650    0.065843       0.688075   \n",
       "\n",
       "                    median_house_value  \n",
       "longitude                    -0.045967  \n",
       "latitude                     -0.144160  \n",
       "housing_median_age            0.105623  \n",
       "total_rooms                   0.134153  \n",
       "total_bedrooms                0.049686  \n",
       "population                   -0.024650  \n",
       "households                    0.065843  \n",
       "median_income                 0.688075  \n",
       "median_house_value            1.000000  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = housing.corr()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "median_income         0.688075\n",
       "total_rooms           0.134153\n",
       "housing_median_age    0.105623\n",
       "households            0.065843\n",
       "total_bedrooms        0.049686\n",
       "population           -0.024650\n",
       "longitude            -0.045967\n",
       "latitude             -0.144160\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix['median_house_value'].sort_values(ascending= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.plotting import scatter_matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x120311b50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1205070d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1205c2c10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1204e63d0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1203cbe50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1203ff550>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1203ff640>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1203aae20>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x122533cd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x122569490>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x12056ac10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1206023d0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x122581b50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1225b8310>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x121b25a90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x121b5c250>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "attribute = ['median_house_value', 'median_income','total_rooms','housing_median_age']\n",
    "scatter_matrix(housing[attribute], figsize=(12,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "housing.plot(kind = 'scatter', y= 'median_house_value', x = 'median_income',alpha=0.1)\n",
    "plt.axis([0,20,0,600000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#50w被设置上限；45，35，28w可能是异常值\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 不同的特征组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \\\n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY   \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY   \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY   \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY   \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY   \n",
       "\n",
       "  income_cat  \n",
       "0          5  \n",
       "1          5  \n",
       "2          5  \n",
       "3          4  \n",
       "4          3  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing['room_per_household'] = housing['total_rooms']/housing['households']\n",
    "housing['bedroom_per_room'] = housing['total_bedrooms']/housing['total_rooms']\n",
    "housing['population_per_household'] = housing['population']/housing['households']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value          1.000000\n",
       "median_income               0.688075\n",
       "room_per_household          0.151948\n",
       "total_rooms                 0.134153\n",
       "housing_median_age          0.105623\n",
       "households                  0.065843\n",
       "total_bedrooms              0.049686\n",
       "population_per_household   -0.023737\n",
       "population                 -0.024650\n",
       "longitude                  -0.045967\n",
       "latitude                   -0.144160\n",
       "bedroom_per_room           -0.255880\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = housing.corr()\n",
    "corr_matrix['median_house_value'].sort_values(ascending= False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 机器学习算法数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>-122.13</td>\n",
       "      <td>37.67</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1748.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>914.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>3.8676</td>\n",
       "      <td>184000.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19529</th>\n",
       "      <td>-120.98</td>\n",
       "      <td>37.65</td>\n",
       "      <td>40.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>7.3841</td>\n",
       "      <td>172200.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8613</th>\n",
       "      <td>-118.37</td>\n",
       "      <td>33.87</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1829.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>891.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>6.5755</td>\n",
       "      <td>359900.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10142</th>\n",
       "      <td>-117.89</td>\n",
       "      <td>33.90</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1533.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>693.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>7.8980</td>\n",
       "      <td>258200.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15867</th>\n",
       "      <td>-122.40</td>\n",
       "      <td>37.76</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1529.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>1347.0</td>\n",
       "      <td>348.0</td>\n",
       "      <td>2.9312</td>\n",
       "      <td>239100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "740      -122.13     37.67                40.0       1748.0           318.0   \n",
       "19529    -120.98     37.65                40.0        422.0            63.0   \n",
       "8613     -118.37     33.87                23.0       1829.0           331.0   \n",
       "10142    -117.89     33.90                23.0       1533.0           226.0   \n",
       "15867    -122.40     37.76                52.0       1529.0           385.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "740         914.0       317.0         3.8676            184000.0   \n",
       "19529       158.0        63.0         7.3841            172200.0   \n",
       "8613        891.0       356.0         6.5755            359900.0   \n",
       "10142       693.0       230.0         7.8980            258200.0   \n",
       "15867      1347.0       348.0         2.9312            239100.0   \n",
       "\n",
       "      ocean_proximity income_cat  \n",
       "740          NEAR BAY          3  \n",
       "19529          INLAND          5  \n",
       "8613        <1H OCEAN          5  \n",
       "10142       <1H OCEAN          5  \n",
       "15867        NEAR BAY          2  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "740      184000.0\n",
       "19529    172200.0\n",
       "8613     359900.0\n",
       "10142    258200.0\n",
       "15867    239100.0\n",
       "           ...   \n",
       "11207    166400.0\n",
       "9035     115100.0\n",
       "10482    330000.0\n",
       "13596     75000.0\n",
       "1318     205100.0\n",
       "Name: median_house_value, Length: 16512, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_labels = strat_train_set['median_house_value'].copy()\n",
    "housing_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>-122.13</td>\n",
       "      <td>37.67</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1748.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>914.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>3.8676</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19529</th>\n",
       "      <td>-120.98</td>\n",
       "      <td>37.65</td>\n",
       "      <td>40.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>7.3841</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8613</th>\n",
       "      <td>-118.37</td>\n",
       "      <td>33.87</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1829.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>891.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>6.5755</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10142</th>\n",
       "      <td>-117.89</td>\n",
       "      <td>33.90</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1533.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>693.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>7.8980</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15867</th>\n",
       "      <td>-122.40</td>\n",
       "      <td>37.76</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1529.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>1347.0</td>\n",
       "      <td>348.0</td>\n",
       "      <td>2.9312</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11207</th>\n",
       "      <td>-117.92</td>\n",
       "      <td>33.83</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1514.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>855.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>3.6042</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9035</th>\n",
       "      <td>-117.84</td>\n",
       "      <td>34.63</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6739.0</td>\n",
       "      <td>1251.0</td>\n",
       "      <td>4614.0</td>\n",
       "      <td>1266.0</td>\n",
       "      <td>4.0020</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10482</th>\n",
       "      <td>-117.69</td>\n",
       "      <td>33.58</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6678.0</td>\n",
       "      <td>1011.0</td>\n",
       "      <td>2877.0</td>\n",
       "      <td>982.0</td>\n",
       "      <td>7.5177</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13596</th>\n",
       "      <td>-117.30</td>\n",
       "      <td>34.10</td>\n",
       "      <td>49.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.5625</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>-121.77</td>\n",
       "      <td>37.99</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5623.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>2429.0</td>\n",
       "      <td>716.0</td>\n",
       "      <td>5.4409</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16512 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "740      -122.13     37.67                40.0       1748.0           318.0   \n",
       "19529    -120.98     37.65                40.0        422.0            63.0   \n",
       "8613     -118.37     33.87                23.0       1829.0           331.0   \n",
       "10142    -117.89     33.90                23.0       1533.0           226.0   \n",
       "15867    -122.40     37.76                52.0       1529.0           385.0   \n",
       "...          ...       ...                 ...          ...             ...   \n",
       "11207    -117.92     33.83                52.0       1514.0           301.0   \n",
       "9035     -117.84     34.63                 5.0       6739.0          1251.0   \n",
       "10482    -117.69     33.58                 5.0       6678.0          1011.0   \n",
       "13596    -117.30     34.10                49.0         60.0            11.0   \n",
       "1318     -121.77     37.99                 4.0       5623.0           780.0   \n",
       "\n",
       "       population  households  median_income ocean_proximity income_cat  \n",
       "740         914.0       317.0         3.8676        NEAR BAY          3  \n",
       "19529       158.0        63.0         7.3841          INLAND          5  \n",
       "8613        891.0       356.0         6.5755       <1H OCEAN          5  \n",
       "10142       693.0       230.0         7.8980       <1H OCEAN          5  \n",
       "15867      1347.0       348.0         2.9312        NEAR BAY          2  \n",
       "...           ...         ...            ...             ...        ...  \n",
       "11207       855.0       293.0         3.6042       <1H OCEAN          3  \n",
       "9035       4614.0      1266.0         4.0020          INLAND          3  \n",
       "10482      2877.0       982.0         7.5177       <1H OCEAN          5  \n",
       "13596        76.0        13.0         2.5625          INLAND          2  \n",
       "1318       2429.0       716.0         5.4409          INLAND          4  \n",
       "\n",
       "[16512 rows x 10 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = strat_train_set.drop(['median_house_value'], axis=1)\n",
    "housing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据清洗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 740 to 1318\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype   \n",
      "---  ------              --------------  -----   \n",
      " 0   longitude           16512 non-null  float64 \n",
      " 1   latitude            16512 non-null  float64 \n",
      " 2   housing_median_age  16512 non-null  float64 \n",
      " 3   total_rooms         16512 non-null  float64 \n",
      " 4   total_bedrooms      16344 non-null  float64 \n",
      " 5   population          16512 non-null  float64 \n",
      " 6   households          16512 non-null  float64 \n",
      " 7   median_income       16512 non-null  float64 \n",
      " 8   ocean_proximity     16512 non-null  object  \n",
      " 9   income_cat          16512 non-null  category\n",
      "dtypes: category(1), float64(8), object(1)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3376</th>\n",
       "      <td>-118.28</td>\n",
       "      <td>34.25</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2559.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1886.0</td>\n",
       "      <td>769.0</td>\n",
       "      <td>2.6036</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11449</th>\n",
       "      <td>-117.98</td>\n",
       "      <td>33.68</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4177.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1704.0</td>\n",
       "      <td>606.0</td>\n",
       "      <td>6.2473</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5665</th>\n",
       "      <td>-118.29</td>\n",
       "      <td>33.73</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3161.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1865.0</td>\n",
       "      <td>771.0</td>\n",
       "      <td>2.7139</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15479</th>\n",
       "      <td>-117.14</td>\n",
       "      <td>33.16</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1660.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>733.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>5.6874</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4043</th>\n",
       "      <td>-118.50</td>\n",
       "      <td>34.17</td>\n",
       "      <td>37.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>369.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>4.1429</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "3376     -118.28     34.25                29.0       2559.0             NaN   \n",
       "11449    -117.98     33.68                24.0       4177.0             NaN   \n",
       "5665     -118.29     33.73                30.0       3161.0             NaN   \n",
       "15479    -117.14     33.16                16.0       1660.0             NaN   \n",
       "4043     -118.50     34.17                37.0        880.0             NaN   \n",
       "\n",
       "       population  households  median_income ocean_proximity income_cat  \n",
       "3376       1886.0       769.0         2.6036       <1H OCEAN          2  \n",
       "11449      1704.0       606.0         6.2473       <1H OCEAN          5  \n",
       "5665       1865.0       771.0         2.7139      NEAR OCEAN          2  \n",
       "15479       733.0       214.0         5.6874       <1H OCEAN          4  \n",
       "4043        369.0       155.0         4.1429       <1H OCEAN          3  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = housing[housing.isnull().any(axis=1)]\n",
    "rows.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(strategy='median')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "impute = SimpleImputer(strategy='median')\n",
    "housing_num = housing.drop('ocean_proximity',axis=1)\n",
    "impute.fit(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.49   ,   34.26   ,   29.     , 2122.5    ,  434.     ,\n",
       "       1163.     ,  409.     ,    3.52945,    3.     ])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "impute.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-122.13  ,   37.67  ,   40.    , ...,  317.    ,    3.8676,\n",
       "           3.    ],\n",
       "       [-120.98  ,   37.65  ,   40.    , ...,   63.    ,    7.3841,\n",
       "           5.    ],\n",
       "       [-118.37  ,   33.87  ,   23.    , ...,  356.    ,    6.5755,\n",
       "           5.    ],\n",
       "       ...,\n",
       "       [-117.69  ,   33.58  ,    5.    , ...,  982.    ,    7.5177,\n",
       "           5.    ],\n",
       "       [-117.3   ,   34.1   ,   49.    , ...,   13.    ,    2.5625,\n",
       "           2.    ],\n",
       "       [-121.77  ,   37.99  ,    4.    , ...,  716.    ,    5.4409,\n",
       "           4.    ]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = impute.transform(housing_num)\n",
    "X\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>-122.13</td>\n",
       "      <td>37.67</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1748.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>914.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>3.8676</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19529</th>\n",
       "      <td>-120.98</td>\n",
       "      <td>37.65</td>\n",
       "      <td>40.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>7.3841</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8613</th>\n",
       "      <td>-118.37</td>\n",
       "      <td>33.87</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1829.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>891.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>6.5755</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10142</th>\n",
       "      <td>-117.89</td>\n",
       "      <td>33.90</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1533.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>693.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>7.8980</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15867</th>\n",
       "      <td>-122.40</td>\n",
       "      <td>37.76</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1529.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>1347.0</td>\n",
       "      <td>348.0</td>\n",
       "      <td>2.9312</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "740      -122.13     37.67                40.0       1748.0           318.0   \n",
       "19529    -120.98     37.65                40.0        422.0            63.0   \n",
       "8613     -118.37     33.87                23.0       1829.0           331.0   \n",
       "10142    -117.89     33.90                23.0       1533.0           226.0   \n",
       "15867    -122.40     37.76                52.0       1529.0           385.0   \n",
       "\n",
       "       population  households  median_income  income_cat  \n",
       "740         914.0       317.0         3.8676         3.0  \n",
       "19529       158.0        63.0         7.3841         5.0  \n",
       "8613        891.0       356.0         6.5755         5.0  \n",
       "10142       693.0       230.0         7.8980         5.0  \n",
       "15867      1347.0       348.0         2.9312         2.0  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr = pd.DataFrame(X, columns=housing_num.columns, index= housing.index)\n",
    "housing_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 740 to 1318\n",
      "Data columns (total 9 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           16512 non-null  float64\n",
      " 1   latitude            16512 non-null  float64\n",
      " 2   housing_median_age  16512 non-null  float64\n",
      " 3   total_rooms         16512 non-null  float64\n",
      " 4   total_bedrooms      16512 non-null  float64\n",
      " 5   population          16512 non-null  float64\n",
      " 6   households          16512 non-null  float64\n",
      " 7   median_income       16512 non-null  float64\n",
      " 8   income_cat          16512 non-null  float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 1.9 MB\n"
     ]
    }
   ],
   "source": [
    "housing_tr.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19529</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8613</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10142</th>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15867</th>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ocean_proximity\n",
       "740          NEAR BAY\n",
       "19529          INLAND\n",
       "8613        <1H OCEAN\n",
       "10142       <1H OCEAN\n",
       "15867        NEAR BAY"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat = housing[['ocean_proximity']]\n",
    "housing_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 0, ..., 0, 1, 1])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder = LabelEncoder()\n",
    "housing_cat = housing['ocean_proximity']\n",
    "housing_cat_encoser=encoder.fit_transform(housing_cat)\n",
    "housing_cat_encoser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "encoder = OneHotEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3],\n",
       "       [1],\n",
       "       [0],\n",
       "       ...,\n",
       "       [0],\n",
       "       [1],\n",
       "       [1]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_encoser.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_hot = encoder.fit_transform(housing_cat_encoser.reshape(-1,1))\n",
    "housing_cat_hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 1., 0.],\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       ...,\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0.]])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_hot.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelBinarizer\n",
    "encoder = LabelBinarizer(sparse_output=True)\n",
    "housing_cat_hot1 = encoder.fit_transform(housing_cat)\n",
    "housing_cat_hot1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 自定义转化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4, 5, 6)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "rooms_ix, bedrooms_ix,population_ix,household_ix = [list(housing.columns).index(col) for col in (\"total_rooms\",\"total_bedrooms\",\"population\",\"households\")]\n",
    "rooms_ix, bedrooms_ix,population_ix,household_ix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CombinedAtrributesAdder(BaseEstimator,TransformerMixin):\n",
    "    def __init__(self,add_bedroom_per_room = True):\n",
    "        self.add_bedroom_per_room = add_bedroom_per_room\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X,y=None):\n",
    "        room_per_household = X[:,rooms_ix]/X[:,household_ix]\n",
    "        population_per_household = X[:,population_ix]/X[:,household_ix]\n",
    "        if self.add_bedroom_per_room:\n",
    "            bedroom_per_room = X[:,bedrooms_ix]/X[:,rooms_ix]\n",
    "            return np.c_[X, room_per_household,population_per_household,bedroom_per_room]\n",
    "        else:\n",
    "            return np.c_[X, room_per_household,population_per_household]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.13</td>\n",
       "      <td>37.67</td>\n",
       "      <td>40</td>\n",
       "      <td>1748</td>\n",
       "      <td>318</td>\n",
       "      <td>914</td>\n",
       "      <td>317</td>\n",
       "      <td>3.8676</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3</td>\n",
       "      <td>5.5142</td>\n",
       "      <td>2.88328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-120.98</td>\n",
       "      <td>37.65</td>\n",
       "      <td>40</td>\n",
       "      <td>422</td>\n",
       "      <td>63</td>\n",
       "      <td>158</td>\n",
       "      <td>63</td>\n",
       "      <td>7.3841</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>5</td>\n",
       "      <td>6.69841</td>\n",
       "      <td>2.50794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-118.37</td>\n",
       "      <td>33.87</td>\n",
       "      <td>23</td>\n",
       "      <td>1829</td>\n",
       "      <td>331</td>\n",
       "      <td>891</td>\n",
       "      <td>356</td>\n",
       "      <td>6.5755</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>5.13764</td>\n",
       "      <td>2.50281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-117.89</td>\n",
       "      <td>33.9</td>\n",
       "      <td>23</td>\n",
       "      <td>1533</td>\n",
       "      <td>226</td>\n",
       "      <td>693</td>\n",
       "      <td>230</td>\n",
       "      <td>7.898</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>6.66522</td>\n",
       "      <td>3.01304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.4</td>\n",
       "      <td>37.76</td>\n",
       "      <td>52</td>\n",
       "      <td>1529</td>\n",
       "      <td>385</td>\n",
       "      <td>1347</td>\n",
       "      <td>348</td>\n",
       "      <td>2.9312</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2</td>\n",
       "      <td>4.39368</td>\n",
       "      <td>3.87069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0      1   2     3    4     5    6       7          8  9       10  \\\n",
       "0 -122.13  37.67  40  1748  318   914  317  3.8676   NEAR BAY  3   5.5142   \n",
       "1 -120.98  37.65  40   422   63   158   63  7.3841     INLAND  5  6.69841   \n",
       "2 -118.37  33.87  23  1829  331   891  356  6.5755  <1H OCEAN  5  5.13764   \n",
       "3 -117.89   33.9  23  1533  226   693  230   7.898  <1H OCEAN  5  6.66522   \n",
       "4  -122.4  37.76  52  1529  385  1347  348  2.9312   NEAR BAY  2  4.39368   \n",
       "\n",
       "        11  \n",
       "0  2.88328  \n",
       "1  2.50794  \n",
       "2  2.50281  \n",
       "3  3.01304  \n",
       "4  3.87069  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Attr_adder = CombinedAtrributesAdder(add_bedroom_per_room =False)\n",
    "housing_extra_attribus = Attr_adder.transform(housing.values)\n",
    "housing_tr = pd.DataFrame(housing_extra_attribus)\n",
    "housing_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "      <th>room_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>-122.13</td>\n",
       "      <td>37.67</td>\n",
       "      <td>40</td>\n",
       "      <td>1748</td>\n",
       "      <td>318</td>\n",
       "      <td>914</td>\n",
       "      <td>317</td>\n",
       "      <td>3.8676</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3</td>\n",
       "      <td>5.5142</td>\n",
       "      <td>2.88328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19529</th>\n",
       "      <td>-120.98</td>\n",
       "      <td>37.65</td>\n",
       "      <td>40</td>\n",
       "      <td>422</td>\n",
       "      <td>63</td>\n",
       "      <td>158</td>\n",
       "      <td>63</td>\n",
       "      <td>7.3841</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>5</td>\n",
       "      <td>6.69841</td>\n",
       "      <td>2.50794</td>\n",
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       "    <tr>\n",
       "      <th>8613</th>\n",
       "      <td>-118.37</td>\n",
       "      <td>33.87</td>\n",
       "      <td>23</td>\n",
       "      <td>1829</td>\n",
       "      <td>331</td>\n",
       "      <td>891</td>\n",
       "      <td>356</td>\n",
       "      <td>6.5755</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>5.13764</td>\n",
       "      <td>2.50281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10142</th>\n",
       "      <td>-117.89</td>\n",
       "      <td>33.9</td>\n",
       "      <td>23</td>\n",
       "      <td>1533</td>\n",
       "      <td>226</td>\n",
       "      <td>693</td>\n",
       "      <td>230</td>\n",
       "      <td>7.898</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>6.66522</td>\n",
       "      <td>3.01304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15867</th>\n",
       "      <td>-122.4</td>\n",
       "      <td>37.76</td>\n",
       "      <td>52</td>\n",
       "      <td>1529</td>\n",
       "      <td>385</td>\n",
       "      <td>1347</td>\n",
       "      <td>348</td>\n",
       "      <td>2.9312</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2</td>\n",
       "      <td>4.39368</td>\n",
       "      <td>3.87069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      longitude latitude housing_median_age total_rooms total_bedrooms  \\\n",
       "740     -122.13    37.67                 40        1748            318   \n",
       "19529   -120.98    37.65                 40         422             63   \n",
       "8613    -118.37    33.87                 23        1829            331   \n",
       "10142   -117.89     33.9                 23        1533            226   \n",
       "15867    -122.4    37.76                 52        1529            385   \n",
       "\n",
       "      population households median_income ocean_proximity income_cat  \\\n",
       "740          914        317        3.8676        NEAR BAY          3   \n",
       "19529        158         63        7.3841          INLAND          5   \n",
       "8613         891        356        6.5755       <1H OCEAN          5   \n",
       "10142        693        230         7.898       <1H OCEAN          5   \n",
       "15867       1347        348        2.9312        NEAR BAY          2   \n",
       "\n",
       "      room_per_household population_per_household  \n",
       "740               5.5142                  2.88328  \n",
       "19529            6.69841                  2.50794  \n",
       "8613             5.13764                  2.50281  \n",
       "10142            6.66522                  3.01304  \n",
       "15867            4.39368                  3.87069  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribus = pd.DataFrame(\n",
    "    housing_extra_attribus, columns=list(housing.columns)+['room_per_household','population_per_household'],index = housing.index)\n",
    "housing_extra_attribus.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "      <th>room_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6216</th>\n",
       "      <td>-117.92</td>\n",
       "      <td>34.06</td>\n",
       "      <td>34</td>\n",
       "      <td>2819</td>\n",
       "      <td>609</td>\n",
       "      <td>1718</td>\n",
       "      <td>558</td>\n",
       "      <td>3.5547</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>5.05197</td>\n",
       "      <td>3.07885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2982</th>\n",
       "      <td>-119.03</td>\n",
       "      <td>35.33</td>\n",
       "      <td>21</td>\n",
       "      <td>3057</td>\n",
       "      <td>698</td>\n",
       "      <td>1627</td>\n",
       "      <td>680</td>\n",
       "      <td>2.7083</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>4.49559</td>\n",
       "      <td>2.39265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19144</th>\n",
       "      <td>-122.7</td>\n",
       "      <td>38.33</td>\n",
       "      <td>26</td>\n",
       "      <td>1887</td>\n",
       "      <td>381</td>\n",
       "      <td>1060</td>\n",
       "      <td>364</td>\n",
       "      <td>3.0078</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>5.18407</td>\n",
       "      <td>2.91209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10310</th>\n",
       "      <td>-117.76</td>\n",
       "      <td>33.88</td>\n",
       "      <td>9</td>\n",
       "      <td>4838</td>\n",
       "      <td>759</td>\n",
       "      <td>2090</td>\n",
       "      <td>695</td>\n",
       "      <td>6.6536</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>6.96115</td>\n",
       "      <td>3.00719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14630</th>\n",
       "      <td>-117.18</td>\n",
       "      <td>32.77</td>\n",
       "      <td>23</td>\n",
       "      <td>1215</td>\n",
       "      <td>225</td>\n",
       "      <td>592</td>\n",
       "      <td>224</td>\n",
       "      <td>3.4</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>5.42411</td>\n",
       "      <td>2.64286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16268</th>\n",
       "      <td>-121.24</td>\n",
       "      <td>37.96</td>\n",
       "      <td>29</td>\n",
       "      <td>874</td>\n",
       "      <td>217</td>\n",
       "      <td>788</td>\n",
       "      <td>222</td>\n",
       "      <td>1.9187</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>3.93694</td>\n",
       "      <td>3.54955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6505</th>\n",
       "      <td>-118.06</td>\n",
       "      <td>34.08</td>\n",
       "      <td>34</td>\n",
       "      <td>1197</td>\n",
       "      <td>260</td>\n",
       "      <td>942</td>\n",
       "      <td>245</td>\n",
       "      <td>3.4202</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.88571</td>\n",
       "      <td>3.8449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5421</th>\n",
       "      <td>-118.43</td>\n",
       "      <td>34.02</td>\n",
       "      <td>41</td>\n",
       "      <td>2403</td>\n",
       "      <td>516</td>\n",
       "      <td>1001</td>\n",
       "      <td>514</td>\n",
       "      <td>4.3906</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.6751</td>\n",
       "      <td>1.94747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18255</th>\n",
       "      <td>-122.09</td>\n",
       "      <td>37.38</td>\n",
       "      <td>34</td>\n",
       "      <td>1959</td>\n",
       "      <td>342</td>\n",
       "      <td>849</td>\n",
       "      <td>357</td>\n",
       "      <td>6.2884</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "      <td>5.48739</td>\n",
       "      <td>2.37815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12978</th>\n",
       "      <td>-121.29</td>\n",
       "      <td>38.68</td>\n",
       "      <td>12</td>\n",
       "      <td>5098</td>\n",
       "      <td>1094</td>\n",
       "      <td>2029</td>\n",
       "      <td>1065</td>\n",
       "      <td>3.5444</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "      <td>4.78685</td>\n",
       "      <td>1.90516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3993</th>\n",
       "      <td>-118.57</td>\n",
       "      <td>34.18</td>\n",
       "      <td>36</td>\n",
       "      <td>2981</td>\n",
       "      <td>441</td>\n",
       "      <td>1243</td>\n",
       "      <td>413</td>\n",
       "      <td>6.5304</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>7.21792</td>\n",
       "      <td>3.00969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20107</th>\n",
       "      <td>-120.32</td>\n",
       "      <td>37.91</td>\n",
       "      <td>16</td>\n",
       "      <td>108</td>\n",
       "      <td>18</td>\n",
       "      <td>54</td>\n",
       "      <td>22</td>\n",
       "      <td>4.375</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "      <td>4.90909</td>\n",
       "      <td>2.45455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19216</th>\n",
       "      <td>-122.68</td>\n",
       "      <td>38.48</td>\n",
       "      <td>15</td>\n",
       "      <td>1575</td>\n",
       "      <td>262</td>\n",
       "      <td>716</td>\n",
       "      <td>259</td>\n",
       "      <td>5.3409</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4</td>\n",
       "      <td>6.08108</td>\n",
       "      <td>2.76448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16425</th>\n",
       "      <td>-121.47</td>\n",
       "      <td>38.13</td>\n",
       "      <td>13</td>\n",
       "      <td>3192</td>\n",
       "      <td>715</td>\n",
       "      <td>1768</td>\n",
       "      <td>626</td>\n",
       "      <td>2.2619</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.09904</td>\n",
       "      <td>2.82428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13879</th>\n",
       "      <td>-117.38</td>\n",
       "      <td>34.44</td>\n",
       "      <td>4</td>\n",
       "      <td>5083</td>\n",
       "      <td>867</td>\n",
       "      <td>2541</td>\n",
       "      <td>856</td>\n",
       "      <td>4.2414</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "      <td>5.93808</td>\n",
       "      <td>2.96846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16134</th>\n",
       "      <td>-122.49</td>\n",
       "      <td>37.78</td>\n",
       "      <td>46</td>\n",
       "      <td>3304</td>\n",
       "      <td>792</td>\n",
       "      <td>1783</td>\n",
       "      <td>777</td>\n",
       "      <td>3.6148</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3</td>\n",
       "      <td>4.25225</td>\n",
       "      <td>2.29472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5719</th>\n",
       "      <td>-118.24</td>\n",
       "      <td>34.22</td>\n",
       "      <td>36</td>\n",
       "      <td>2507</td>\n",
       "      <td>517</td>\n",
       "      <td>1232</td>\n",
       "      <td>470</td>\n",
       "      <td>5.529</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4</td>\n",
       "      <td>5.33404</td>\n",
       "      <td>2.62128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5340</th>\n",
       "      <td>-118.45</td>\n",
       "      <td>34.03</td>\n",
       "      <td>39</td>\n",
       "      <td>1657</td>\n",
       "      <td>402</td>\n",
       "      <td>931</td>\n",
       "      <td>363</td>\n",
       "      <td>3.7813</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.56474</td>\n",
       "      <td>2.56474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12423</th>\n",
       "      <td>-116.15</td>\n",
       "      <td>33.69</td>\n",
       "      <td>22</td>\n",
       "      <td>197</td>\n",
       "      <td>54</td>\n",
       "      <td>331</td>\n",
       "      <td>70</td>\n",
       "      <td>2.9286</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>2.81429</td>\n",
       "      <td>4.72857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6149</th>\n",
       "      <td>-117.97</td>\n",
       "      <td>34.1</td>\n",
       "      <td>26</td>\n",
       "      <td>1399</td>\n",
       "      <td>277</td>\n",
       "      <td>1285</td>\n",
       "      <td>276</td>\n",
       "      <td>4</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "      <td>5.06884</td>\n",
       "      <td>4.6558</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      longitude latitude housing_median_age total_rooms total_bedrooms  \\\n",
       "6216    -117.92    34.06                 34        2819            609   \n",
       "2982    -119.03    35.33                 21        3057            698   \n",
       "19144    -122.7    38.33                 26        1887            381   \n",
       "10310   -117.76    33.88                  9        4838            759   \n",
       "14630   -117.18    32.77                 23        1215            225   \n",
       "16268   -121.24    37.96                 29         874            217   \n",
       "6505    -118.06    34.08                 34        1197            260   \n",
       "5421    -118.43    34.02                 41        2403            516   \n",
       "18255   -122.09    37.38                 34        1959            342   \n",
       "12978   -121.29    38.68                 12        5098           1094   \n",
       "3993    -118.57    34.18                 36        2981            441   \n",
       "20107   -120.32    37.91                 16         108             18   \n",
       "19216   -122.68    38.48                 15        1575            262   \n",
       "16425   -121.47    38.13                 13        3192            715   \n",
       "13879   -117.38    34.44                  4        5083            867   \n",
       "16134   -122.49    37.78                 46        3304            792   \n",
       "5719    -118.24    34.22                 36        2507            517   \n",
       "5340    -118.45    34.03                 39        1657            402   \n",
       "12423   -116.15    33.69                 22         197             54   \n",
       "6149    -117.97     34.1                 26        1399            277   \n",
       "\n",
       "      population households median_income ocean_proximity income_cat  \\\n",
       "6216        1718        558        3.5547       <1H OCEAN          3   \n",
       "2982        1627        680        2.7083          INLAND          2   \n",
       "19144       1060        364        3.0078       <1H OCEAN          3   \n",
       "10310       2090        695        6.6536       <1H OCEAN          5   \n",
       "14630        592        224           3.4      NEAR OCEAN          3   \n",
       "16268        788        222        1.9187          INLAND          2   \n",
       "6505         942        245        3.4202       <1H OCEAN          3   \n",
       "5421        1001        514        4.3906       <1H OCEAN          3   \n",
       "18255        849        357        6.2884        NEAR BAY          5   \n",
       "12978       2029       1065        3.5444          INLAND          3   \n",
       "3993        1243        413        6.5304       <1H OCEAN          5   \n",
       "20107         54         22         4.375          INLAND          3   \n",
       "19216        716        259        5.3409       <1H OCEAN          4   \n",
       "16425       1768        626        2.2619          INLAND          2   \n",
       "13879       2541        856        4.2414          INLAND          3   \n",
       "16134       1783        777        3.6148        NEAR BAY          3   \n",
       "5719        1232        470         5.529       <1H OCEAN          4   \n",
       "5340         931        363        3.7813       <1H OCEAN          3   \n",
       "12423        331         70        2.9286          INLAND          2   \n",
       "6149        1285        276             4          INLAND          3   \n",
       "\n",
       "      room_per_household population_per_household  \n",
       "6216             5.05197                  3.07885  \n",
       "2982             4.49559                  2.39265  \n",
       "19144            5.18407                  2.91209  \n",
       "10310            6.96115                  3.00719  \n",
       "14630            5.42411                  2.64286  \n",
       "16268            3.93694                  3.54955  \n",
       "6505             4.88571                   3.8449  \n",
       "5421              4.6751                  1.94747  \n",
       "18255            5.48739                  2.37815  \n",
       "12978            4.78685                  1.90516  \n",
       "3993             7.21792                  3.00969  \n",
       "20107            4.90909                  2.45455  \n",
       "19216            6.08108                  2.76448  \n",
       "16425            5.09904                  2.82428  \n",
       "13879            5.93808                  2.96846  \n",
       "16134            4.25225                  2.29472  \n",
       "5719             5.33404                  2.62128  \n",
       "5340             4.56474                  2.56474  \n",
       "12423            2.81429                  4.72857  \n",
       "6149             5.06884                   4.6558  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribus.sample(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征缩放 转换流水线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "    ('imputer',SimpleImputer(strategy= 'median')),\n",
    "    ('attribs_adder', CombinedAtrributesAdder()),\n",
    "    ('std_scaler',StandardScaler())\n",
    "])\n",
    "\n",
    "housing_num_tr = num_pipeline.fit_transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataFrameSelector(BaseEstimator,TransformerMixin):\n",
    "    def __init__(self, attribute_name):\n",
    "        self.attribute_name = attribute_name\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[self.attribute_name].values\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['longitude',\n",
       " 'latitude',\n",
       " 'housing_median_age',\n",
       " 'total_rooms',\n",
       " 'total_bedrooms',\n",
       " 'population',\n",
       " 'households',\n",
       " 'median_income',\n",
       " 'income_cat']"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_attribs = list(housing_num)\n",
    "num_attribs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pipeline = Pipeline([\n",
    "    ('selector',DataFrameSelector(num_attribs)),\n",
    "    ('imputer',SimpleImputer(strategy= 'median')),\n",
    "    ('attribs_adder', CombinedAtrributesAdder()),\n",
    "    ('std_scaler',StandardScaler()),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import TransformerMixin\n",
    "class MyLabelBinarizer(TransformerMixin):\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        self.encoder = LabelBinarizer(*args, **kwargs)\n",
    "    def fit(self, x, y=0):\n",
    "        self.encoder.fit(x)\n",
    "        return self\n",
    "    def transform(self, x, y=0):\n",
    "        return self.encoder.transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_attribs = ['ocean_proximity']\n",
    "\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "cat_pipeline = Pipeline([\n",
    "    ('selector', DataFrameSelector(cat_attribs)),\n",
    "    ('LabelBinarizer', MyLabelBinarizer()),\n",
    "])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import FeatureUnion\n",
    "\n",
    "full_pipeline = FeatureUnion(transformer_list=[\n",
    "    ('num_pipeline',num_pipeline,),\n",
    "    ('cat_pipeline',cat_pipeline,)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.27826235,  0.95445204,  0.89646428, ...,  0.        ,\n",
       "         1.        ,  0.        ],\n",
       "       [-0.70432019,  0.94509343,  0.89646428, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.59827896, -0.82368426, -0.45394013, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       ...,\n",
       "       [ 0.93765346, -0.95938413, -1.88378009, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 1.13229471, -0.71606022,  1.61138426, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.0985935 ,  1.10418984, -1.96321564, ...,  0.        ,\n",
       "         0.        ,  0.        ]])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_finished = full_pipeline.fit_transform(housing)\n",
    "housing_finished"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "      <th>room_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "      <th>one_hot1</th>\n",
       "      <th>one_hot2</th>\n",
       "      <th>one_hot3</th>\n",
       "      <th>one_hot4</th>\n",
       "      <th>one_hot5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>-1.278262</td>\n",
       "      <td>0.954452</td>\n",
       "      <td>0.896464</td>\n",
       "      <td>-0.404817</td>\n",
       "      <td>-0.520170</td>\n",
       "      <td>-0.453767</td>\n",
       "      <td>-0.476899</td>\n",
       "      <td>-0.001824</td>\n",
       "      <td>-0.006202</td>\n",
       "      <td>0.044356</td>\n",
       "      <td>-0.016937</td>\n",
       "      <td>-0.491753</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19529</th>\n",
       "      <td>-0.704320</td>\n",
       "      <td>0.945093</td>\n",
       "      <td>0.896464</td>\n",
       "      <td>-1.008704</td>\n",
       "      <td>-1.126010</td>\n",
       "      <td>-1.124920</td>\n",
       "      <td>-1.139412</td>\n",
       "      <td>1.839337</td>\n",
       "      <td>1.890305</td>\n",
       "      <td>0.565635</td>\n",
       "      <td>-0.051355</td>\n",
       "      <td>-0.996460</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8613</th>\n",
       "      <td>0.598279</td>\n",
       "      <td>-0.823684</td>\n",
       "      <td>-0.453940</td>\n",
       "      <td>-0.367928</td>\n",
       "      <td>-0.489285</td>\n",
       "      <td>-0.474186</td>\n",
       "      <td>-0.375175</td>\n",
       "      <td>1.415972</td>\n",
       "      <td>1.890305</td>\n",
       "      <td>-0.121399</td>\n",
       "      <td>-0.051825</td>\n",
       "      <td>-0.506430</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10142</th>\n",
       "      <td>0.837837</td>\n",
       "      <td>-0.809646</td>\n",
       "      <td>-0.453940</td>\n",
       "      <td>-0.502732</td>\n",
       "      <td>-0.738748</td>\n",
       "      <td>-0.649964</td>\n",
       "      <td>-0.703823</td>\n",
       "      <td>2.108404</td>\n",
       "      <td>1.890305</td>\n",
       "      <td>0.551023</td>\n",
       "      <td>-0.005038</td>\n",
       "      <td>-1.025316</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15867</th>\n",
       "      <td>-1.413014</td>\n",
       "      <td>0.996566</td>\n",
       "      <td>1.849691</td>\n",
       "      <td>-0.504554</td>\n",
       "      <td>-0.360989</td>\n",
       "      <td>-0.069363</td>\n",
       "      <td>-0.396042</td>\n",
       "      <td>-0.492102</td>\n",
       "      <td>-0.954456</td>\n",
       "      <td>-0.448884</td>\n",
       "      <td>0.073605</td>\n",
       "      <td>0.588964</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "740    -1.278262  0.954452            0.896464    -0.404817       -0.520170   \n",
       "19529  -0.704320  0.945093            0.896464    -1.008704       -1.126010   \n",
       "8613    0.598279 -0.823684           -0.453940    -0.367928       -0.489285   \n",
       "10142   0.837837 -0.809646           -0.453940    -0.502732       -0.738748   \n",
       "15867  -1.413014  0.996566            1.849691    -0.504554       -0.360989   \n",
       "\n",
       "       population  households  median_income  ocean_proximity  income_cat  \\\n",
       "740     -0.453767   -0.476899      -0.001824        -0.006202    0.044356   \n",
       "19529   -1.124920   -1.139412       1.839337         1.890305    0.565635   \n",
       "8613    -0.474186   -0.375175       1.415972         1.890305   -0.121399   \n",
       "10142   -0.649964   -0.703823       2.108404         1.890305    0.551023   \n",
       "15867   -0.069363   -0.396042      -0.492102        -0.954456   -0.448884   \n",
       "\n",
       "       room_per_household  population_per_household  one_hot1  one_hot2  \\\n",
       "740             -0.016937                 -0.491753       0.0       0.0   \n",
       "19529           -0.051355                 -0.996460       0.0       1.0   \n",
       "8613            -0.051825                 -0.506430       1.0       0.0   \n",
       "10142           -0.005038                 -1.025316       1.0       0.0   \n",
       "15867            0.073605                  0.588964       0.0       0.0   \n",
       "\n",
       "       one_hot3  one_hot4  one_hot5  \n",
       "740         0.0       1.0       0.0  \n",
       "19529       0.0       0.0       0.0  \n",
       "8613        0.0       0.0       0.0  \n",
       "10142       0.0       0.0       0.0  \n",
       "15867       0.0       1.0       0.0  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared = pd.DataFrame(housing_finished,columns=list(housing.columns)+['room_per_household','population_per_household','one_hot1','one_hot2','one_hot3','one_hot4','one_hot5'],index = housing.index)\n",
    "housing_prepared.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "740      184000.0\n",
       "19529    172200.0\n",
       "8613     359900.0\n",
       "10142    258200.0\n",
       "15867    239100.0\n",
       "           ...   \n",
       "11207    166400.0\n",
       "9035     115100.0\n",
       "10482    330000.0\n",
       "13596     75000.0\n",
       "1318     205100.0\n",
       "Name: median_house_value, Length: 16512, dtype: float64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lin_reg = LinearRegression()\n",
    "\n",
    "lin_reg.fit(housing_prepared,housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions: [234037.47081607 310620.60423894 336984.99545218 356489.66129607\n",
      " 203774.32795588]\n"
     ]
    }
   ],
   "source": [
    "some_data = housing.iloc[:5]\n",
    "some_labels = housing_labels.iloc[:5]\n",
    "\n",
    "some_data_pre = full_pipeline.transform(some_data)\n",
    "print('predictions:', lin_reg.predict(some_data_pre))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4701772039.360102"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "housing_pre = lin_reg.predict(housing_prepared)\n",
    "lin_mse = mean_squared_error(housing_labels,housing_pre)\n",
    "lin_mse\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68569.46871137402"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_rmse = np.sqrt(lin_mse)\n",
    "lin_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(random_state=1)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor(random_state= 1)\n",
    "tree_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_pre = tree_reg.predict(housing_prepared)\n",
    "tree_mse = mean_squared_error(housing_labels,housing_pre)\n",
    "tree_rmse = np.sqrt(tree_mse)\n",
    "tree_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([71369.36597507, 71549.95332791, 70649.66880005, 68835.73421255,\n",
       "       73115.7309118 , 67164.14508875, 72879.01771716, 68997.35574016,\n",
       "       71920.91568143, 71457.41590443])"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 使用交叉验证\n",
    "###决策树交叉验证\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "scores = cross_val_score(tree_reg, housing_prepared, housing_labels, scoring='neg_mean_squared_error', cv=10)\n",
    "tree_rmse_scores = np.sqrt(-scores)\n",
    "tree_rmse_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([70709.44027577, 66095.21468652, 73471.19391288, 69652.40994218,\n",
       "       69144.9262453 , 68640.96403507, 65591.88734176, 67028.957668  ,\n",
       "       73043.51241446, 66560.94444563])"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 线性交叉验证\n",
    "\n",
    "scores = cross_val_score(lin_reg, housing_prepared, housing_labels, scoring='neg_mean_squared_error', cv=10)\n",
    "lin_rmse_scores = np.sqrt(-scores)\n",
    "lin_rmse_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "68993.94509675886\n"
     ]
    }
   ],
   "source": [
    "print(lin_rmse_scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70793.93033593151\n"
     ]
    }
   ],
   "source": [
    "print(tree_rmse_scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(n_estimators=10, random_state=1)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor(n_estimators=10, random_state=1)\n",
    "forest_reg.fit(housing_prepared,housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22608.28819505216"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_pre = forest_reg.predict(housing_prepared)\n",
    "forest_mse = mean_squared_error(housing_labels,housing_pre)\n",
    "forest_rmse = np.sqrt(forest_mse)\n",
    "forest_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([54303.85062914, 48989.06366422, 50983.23878802, 52711.30028124,\n",
       "       53831.64122159, 52776.30631419, 51870.97186579, 50674.42967171,\n",
       "       55844.83879971, 52054.59280227])"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = cross_val_score(forest_reg, housing_prepared, housing_labels, scoring='neg_mean_squared_error', cv=10)\n",
    "forest_rmse_scores = np.sqrt(-scores)\n",
    "forest_rmse_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestRegressor(random_state=1),\n",
       "             param_grid=[{'max_features': [2, 4, 6, 8],\n",
       "                          'n_estimators': [3, 10, 30]},\n",
       "                         {'bootstrap': [False], 'max_features': [2, 3, 4],\n",
       "                          'n_estimators': [3, 10]}],\n",
       "             return_train_score=True, scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "    {'n_estimators':[3, 10, 30], 'max_features':[2, 4, 6, 8]},\n",
    "    {'bootstrap':[False],'n_estimators':[3, 10], 'max_features':[2, 3, 4]}\n",
    "]\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=1)\n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv = 5, scoring='neg_mean_squared_error', return_train_score=True)\n",
    "grid_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_features=8, n_estimators=30, random_state=1)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 8, 'n_estimators': 30}"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.05843796, 0.05558843, 0.04788793, 0.01624957, 0.01600288,\n",
       "       0.01702292, 0.01526328, 0.28734543, 0.13918327, 0.04156305,\n",
       "       0.11300592, 0.04688267, 0.00453769, 0.13471847, 0.00030042,\n",
       "       0.0019901 , 0.00402   ])"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances=grid_search.best_estimator_.feature_importances_\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析最佳模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['longitude',\n",
       " 'latitude',\n",
       " 'housing_median_age',\n",
       " 'total_rooms',\n",
       " 'total_bedrooms',\n",
       " 'population',\n",
       " 'households',\n",
       " 'median_income',\n",
       " 'income_cat']"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_attribs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "      dtype='<U10')"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['longitude',\n",
       " 'latitude',\n",
       " 'housing_median_age',\n",
       " 'total_rooms',\n",
       " 'total_bedrooms',\n",
       " 'population',\n",
       " 'households',\n",
       " 'median_income',\n",
       " 'income_cat',\n",
       " 'room_per_household',\n",
       " 'population_per_household',\n",
       " 'bedroom_per_room',\n",
       " '<1H OCEAN',\n",
       " 'INLAND',\n",
       " 'ISLAND',\n",
       " 'NEAR BAY',\n",
       " 'NEAR OCEAN']"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extra_attributes = ['room_per_household','population_per_household','bedroom_per_room']\n",
    "cat_one_hot_attribs = list(encoder.classes_)\n",
    "attributes = num_attribs+extra_attributes+cat_one_hot_attribs\n",
    "attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.28734543058659356, 'median_income'),\n",
       " (0.1391832694243736, 'income_cat'),\n",
       " (0.13471847016052363, 'INLAND'),\n",
       " (0.11300592482111282, 'population_per_household'),\n",
       " (0.058437956464823373, 'longitude'),\n",
       " (0.0555884333823074, 'latitude'),\n",
       " (0.047887930619066696, 'housing_median_age'),\n",
       " (0.046882670405300475, 'bedroom_per_room'),\n",
       " (0.04156305065056367, 'room_per_household'),\n",
       " (0.017022923685949876, 'population'),\n",
       " (0.016249570261820022, 'total_rooms'),\n",
       " (0.016002876728642285, 'total_bedrooms'),\n",
       " (0.015263281713703048, 'households'),\n",
       " (0.004537693973205233, '<1H OCEAN'),\n",
       " (0.004019997676113861, 'NEAR OCEAN'),\n",
       " (0.0019900973427299773, 'NEAR BAY'),\n",
       " (0.0003004221031705759, 'ISLAND')]"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(zip(feature_importances,attributes),reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 通过测试集评估系统"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48856.91505021107"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model = grid_search.best_estimator_\n",
    "\n",
    "X_test = strat_test_set.drop('median_house_value',axis=1)\n",
    "y_test = strat_test_set['median_house_value'].copy()\n",
    "\n",
    "X_test_prepared = full_pipeline.transform(X_test)\n",
    "\n",
    "final_predictions = final_model.predict(X_test_prepared)\n",
    "\n",
    "final_mse = mean_squared_error(y_test,final_predictions)\n",
    "final_rmse = np.sqrt(final_mse)\n",
    "final_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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